Examples with IntDummyVector com.tencent.angel.ml.math2.vector.IntDummyVector used on opensource projects

Example 26 with IntDummyVector

use of com.tencent.angel.ml.math2.vector.IntDummyVector in project angel by Tencent.

the class MixedBinaryInNonZAExecutor method apply.

private static Vector apply(CompIntLongVector v1, IntDummyVector v2, Binary op) {
    IntLongVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (!op.isKeepStorage()) {
        for (int i = 0; i < parts.length; i++) {
            if (parts[i].getStorage() instanceof IntLongSortedVectorStorage) {
                resParts[i] = new IntLongSparseVectorStorage(parts[i].getDim(), parts[i].getStorage().getIndices(), parts[i].getStorage().getValues());
            }
        }
    }
    int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
    int[] v2Indices = v2.getIndices();
    for (int i = 0; i < v2Indices.length; i++) {
        int gidx = v2Indices[i];
        int pidx = (int) (gidx / subDim);
        int subidx = gidx % subDim;
        ((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
    }
    IntLongVector[] res = new IntLongVector[parts.length];
    int i = 0;
    for (IntLongVector part : parts) {
        res[i] = new IntLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntLongVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 27 with IntDummyVector

use of com.tencent.angel.ml.math2.vector.IntDummyVector in project angel by Tencent.

the class MixedBinaryInZAExecutor method apply.

private static Vector apply(CompIntDoubleVector v1, IntDummyVector v2, Binary op) {
    IntDoubleVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v1.size() > v2.size()) {
        int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
        int[] v2Indices = v2.getIndices();
        for (int i = 0; i < v2Indices.length; i++) {
            int idx = v2Indices[i];
            int pidx = (int) (idx / subDim);
            int subidx = idx % subDim;
            if (parts[pidx].hasKey(subidx)) {
                ((IntDoubleVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
            }
        }
    } else {
        int base = 0;
        for (int i = 0; i < parts.length; i++) {
            IntDoubleVector part = parts[i];
            IntDoubleVectorStorage resPart = (IntDoubleVectorStorage) resParts[i];
            if (part.isDense()) {
                double[] partValues = part.getStorage().getValues();
                double[] resPartValues = resPart.getValues();
                for (int j = 0; j < partValues.length; j++) {
                    if (v2.hasKey(j + base)) {
                        resPartValues[j] = op.apply(partValues[j], v2.get(j + base));
                    }
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2DoubleMap.Entry> piter = part.getStorage().entryIterator();
                while (piter.hasNext()) {
                    Int2DoubleMap.Entry entry = piter.next();
                    int idx = entry.getIntKey();
                    if (v2.hasKey(idx + base)) {
                        resPart.set(idx, op.apply(entry.getDoubleValue(), v2.get(idx + base)));
                    }
                }
            } else {
                // sorted
                if (op.isKeepStorage()) {
                    int[] partIndices = part.getStorage().getIndices();
                    double[] partValues = part.getStorage().getValues();
                    int[] resPartIndices = resPart.getIndices();
                    double[] resPartValues = resPart.getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPartIndices[j] = idx;
                            resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
                        }
                    }
                } else {
                    int[] partIndices = part.getStorage().getIndices();
                    double[] partValues = part.getStorage().getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPart.set(idx, op.apply(partValues[j], v2.get(idx + base)));
                        }
                    }
                }
            }
            base += part.getDim();
        }
    }
    IntDoubleVector[] res = new IntDoubleVector[parts.length];
    int i = 0;
    for (IntDoubleVector part : parts) {
        res[i] = new IntDoubleVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntDoubleVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 28 with IntDummyVector

use of com.tencent.angel.ml.math2.vector.IntDummyVector in project angel by Tencent.

the class MixedBinaryInZAExecutor method apply.

private static Vector apply(CompIntFloatVector v1, IntDummyVector v2, Binary op) {
    IntFloatVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v1.size() > v2.size()) {
        int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
        int[] v2Indices = v2.getIndices();
        for (int i = 0; i < v2Indices.length; i++) {
            int idx = v2Indices[i];
            int pidx = (int) (idx / subDim);
            int subidx = idx % subDim;
            if (parts[pidx].hasKey(subidx)) {
                ((IntFloatVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
            }
        }
    } else {
        int base = 0;
        for (int i = 0; i < parts.length; i++) {
            IntFloatVector part = parts[i];
            IntFloatVectorStorage resPart = (IntFloatVectorStorage) resParts[i];
            if (part.isDense()) {
                float[] partValues = part.getStorage().getValues();
                float[] resPartValues = resPart.getValues();
                for (int j = 0; j < partValues.length; j++) {
                    if (v2.hasKey(j + base)) {
                        resPartValues[j] = op.apply(partValues[j], v2.get(j + base));
                    }
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2FloatMap.Entry> piter = part.getStorage().entryIterator();
                while (piter.hasNext()) {
                    Int2FloatMap.Entry entry = piter.next();
                    int idx = entry.getIntKey();
                    if (v2.hasKey(idx + base)) {
                        resPart.set(idx, op.apply(entry.getFloatValue(), v2.get(idx + base)));
                    }
                }
            } else {
                // sorted
                if (op.isKeepStorage()) {
                    int[] partIndices = part.getStorage().getIndices();
                    float[] partValues = part.getStorage().getValues();
                    int[] resPartIndices = resPart.getIndices();
                    float[] resPartValues = resPart.getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPartIndices[j] = idx;
                            resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
                        }
                    }
                } else {
                    int[] partIndices = part.getStorage().getIndices();
                    float[] partValues = part.getStorage().getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPart.set(idx, op.apply(partValues[j], v2.get(idx + base)));
                        }
                    }
                }
            }
            base += part.getDim();
        }
    }
    IntFloatVector[] res = new IntFloatVector[parts.length];
    int i = 0;
    for (IntFloatVector part : parts) {
        res[i] = new IntFloatVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntFloatVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 29 with IntDummyVector

use of com.tencent.angel.ml.math2.vector.IntDummyVector in project angel by Tencent.

the class MixedBinaryInZAExecutor method apply.

private static Vector apply(CompIntLongVector v1, IntDummyVector v2, Binary op) {
    IntLongVector[] parts = v1.getPartitions();
    Storage[] resParts = StorageSwitch.applyComp(v1, v2, op);
    if (v1.size() > v2.size()) {
        int subDim = (v1.getDim() + v1.getNumPartitions() - 1) / v1.getNumPartitions();
        int[] v2Indices = v2.getIndices();
        for (int i = 0; i < v2Indices.length; i++) {
            int idx = v2Indices[i];
            int pidx = (int) (idx / subDim);
            int subidx = idx % subDim;
            if (parts[pidx].hasKey(subidx)) {
                ((IntLongVectorStorage) resParts[pidx]).set(subidx, op.apply(parts[pidx].get(subidx), 1));
            }
        }
    } else {
        int base = 0;
        for (int i = 0; i < parts.length; i++) {
            IntLongVector part = parts[i];
            IntLongVectorStorage resPart = (IntLongVectorStorage) resParts[i];
            if (part.isDense()) {
                long[] partValues = part.getStorage().getValues();
                long[] resPartValues = resPart.getValues();
                for (int j = 0; j < partValues.length; j++) {
                    if (v2.hasKey(j + base)) {
                        resPartValues[j] = op.apply(partValues[j], v2.get(j + base));
                    }
                }
            } else if (part.isSparse()) {
                ObjectIterator<Int2LongMap.Entry> piter = part.getStorage().entryIterator();
                while (piter.hasNext()) {
                    Int2LongMap.Entry entry = piter.next();
                    int idx = entry.getIntKey();
                    if (v2.hasKey(idx + base)) {
                        resPart.set(idx, op.apply(entry.getLongValue(), v2.get(idx + base)));
                    }
                }
            } else {
                // sorted
                if (op.isKeepStorage()) {
                    int[] partIndices = part.getStorage().getIndices();
                    long[] partValues = part.getStorage().getValues();
                    int[] resPartIndices = resPart.getIndices();
                    long[] resPartValues = resPart.getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPartIndices[j] = idx;
                            resPartValues[j] = op.apply(partValues[j], v2.get(idx + base));
                        }
                    }
                } else {
                    int[] partIndices = part.getStorage().getIndices();
                    long[] partValues = part.getStorage().getValues();
                    for (int j = 0; j < partIndices.length; j++) {
                        int idx = partIndices[j];
                        if (v2.hasKey(idx + base)) {
                            resPart.set(idx, op.apply(partValues[j], v2.get(idx + base)));
                        }
                    }
                }
            }
            base += part.getDim();
        }
    }
    IntLongVector[] res = new IntLongVector[parts.length];
    int i = 0;
    for (IntLongVector part : parts) {
        res[i] = new IntLongVector(part.getMatrixId(), part.getRowId(), part.getClock(), part.getDim(), (IntLongVectorStorage) resParts[i]);
        i++;
    }
    v1.setPartitions(res);
    return v1;
}

Example 30 with IntDummyVector

use of com.tencent.angel.ml.math2.vector.IntDummyVector in project angel by Tencent.

the class BinaryMatrixExecutor method apply.

private static Matrix apply(BlasFloatMatrix mat, IntDummyVector v, int idx, boolean onCol, Binary op) {
    float[] data = mat.getData();
    int m = mat.getNumRows(), n = mat.getNumCols();
    int size = v.size();
    byte[] flag = new byte[v.getDim()];
    if (onCol && op.isInplace()) {
        int[] idxs = v.getIndices();
        for (int k = 0; k < size; k++) {
            int i = idxs[k];
            flag[i] = 1;
            data[i * n + idx] = op.apply(data[i * n + idx], 1);
        }
        if (op.getOpType() == INTERSECTION) {
            for (int i = 0; i < m; i++) {
                if (flag[i] == 0) {
                    data[i * n + idx] = 0;
                }
            }
        } else if (op.getOpType() == ALL) {
            for (int i = 0; i < m; i++) {
                if (flag[i] == 0) {
                    data[i * n + idx] = op.apply(data[i * n + idx], 0);
                }
            }
        }
        return mat;
    } else if (onCol && !op.isInplace()) {
        float[] newData = ArrayCopy.copy(data);
        int[] idxs = v.getIndices();
        for (int k = 0; k < size; k++) {
            int i = idxs[k];
            flag[i] = 1;
            newData[i * n + idx] = op.apply(data[i * n + idx], 1);
        }
        if (op.getOpType() == INTERSECTION) {
            for (int i = 0; i < m; i++) {
                if (flag[i] == 0) {
                    newData[i * n + idx] = 0;
                }
            }
        } else if (op.getOpType() == ALL) {
            for (int i = 0; i < m; i++) {
                if (flag[i] == 0) {
                    newData[i * n + idx] = op.apply(newData[i * n + idx], 0);
                }
            }
        }
        return new BlasFloatMatrix(mat.getMatrixId(), mat.getClock(), m, n, newData);
    } else if (!onCol && op.isInplace()) {
        int[] idxs = v.getIndices();
        for (int k = 0; k < size; k++) {
            int j = idxs[k];
            flag[j] = 1;
            data[idx * n + j] = op.apply(data[idx * n + j], 1);
        }
        if (op.getOpType() == INTERSECTION) {
            for (int j = 0; j < n; j++) {
                if (flag[j] == 0) {
                    data[idx * n + j] = 0;
                }
            }
        } else if (op.getOpType() == ALL) {
            for (int j = 0; j < n; j++) {
                if (flag[j] == 0) {
                    data[idx * n + j] = op.apply(data[idx * n + j], 0);
                }
            }
        }
        return mat;
    } else {
        float[] newData = ArrayCopy.copy(data);
        int[] idxs = v.getIndices();
        for (int k = 0; k < size; k++) {
            int j = idxs[k];
            flag[j] = 1;
            newData[idx * n + j] = op.apply(data[idx * n + j], 1);
        }
        if (op.getOpType() == INTERSECTION) {
            for (int j = 0; j < n; j++) {
                if (flag[j] == 0) {
                    newData[idx * n + j] = 0;
                }
            }
        } else if (op.getOpType() == ALL) {
            for (int j = 0; j < n; j++) {
                if (flag[j] == 0) {
                    newData[idx * n + j] = op.apply(newData[idx * n + j], 0);
                }
            }
        }
        return new BlasFloatMatrix(mat.getMatrixId(), mat.getClock(), m, n, newData);
    }
}